Automated training and exercise adjustments based on sensor-detected exercise form and physiological activation

Abstract

The invention(s) described are configured to process sensor data in order to optimize or otherwise improve training of users for achievement of goals in relation to performing an activity. The invention(s) can also iteratively adapt training in a personalized manner, with assessment of training results and subsequent modification of training regimens, in order to provide improved alignment between users and their goals. Such iteration can drive interventions provided to users throughout the course of training, and allow the system to iteratively develop better and more precise interventions (e.g., through manual means, through machine learning models with generated training and test data). Such iteration, with large datasets applied to populations of users can also increase the breadth of user states that the can be addressed, with respect to provided interventions, and improve rates at which interventions are provided.

What is claimed is: 
     
         1 . A method comprising:
 receiving a set of signals related to user performance of an activity, from a garment worn by a user, the garment including a set of sensors configured to generate the set of signals;   extracting a performance dataset upon processing the set of signals, the performance dataset characterizing form and exertion features across a set of muscles of the user in association with performance of the activity, wherein determining the performance dataset comprises generating, for each of the set of muscles, a value of normalized activation relative to a baseline activation for the set of muscles of the user;   generating, from the performance dataset, an analysis of the user performing the activity based upon a comparison to a reference profile associated with a goal of the user;   generating a recommended action based upon the analysis and executing the recommended action through an exercise feedback system in communication with the garment; and   promoting engagement between the user and the exercise feedback system based upon the analysis and in coordination with executing the recommended action, wherein promoting engagement comprises returning, for communication to the user, an output indicating performance of the user relative to performance of a competitor.   
     
     
         2 . The method of  claim 1 , wherein the set of signals related to user performance of the activity comprises signals derived from one or more of: a subset of the set of muscles being activated, temporal aspects associated with activation of the subset of muscles, and intensity aspects associated with activation of the subset of muscles. 
     
     
         3 . The method of  claim 2 , wherein extracting the performance dataset further comprises: extracting form, balance, and exertion of the user during performance of the activity, upon processing the set of signals with a performance model configured to transform the set of signals into values of features of the performance dataset. 
     
     
         4 . The method of  claim 3 , wherein generating the analysis further comprises generating a characterization of performance of the activity by the user in relation to the reference profile. 
     
     
         5 . The method of  claim 1 , wherein the baseline activation is derived from a maximum exertion value of at least one of the set of muscles of the user. 
     
     
         6 . The method of  claim 1 , wherein the goal of the user is adjusted based on user input associated with a lifestyle constraint of the user. 
     
     
         7 . The method of  claim 1 , wherein the recommended action comprises a rendering of digital objects informative of user performance toward the goal at a second resolution more detailed than a first resolution associated with physical observation of the user, wherein the rendering is provided to a client device. 
     
     
         8 . The method of  claim 1 , wherein the recommended action comprises sharing features derived from the performance dataset of the user with a cohort of entities associated with the user, within an online social network platform. 
     
     
         9 . The method of  claim 1 , wherein the reference profile comprises a set of target activation features derived from an athlete performing the activity. 
     
     
         10 . The method of  claim 1 , wherein the recommended action comprises a set of control instructions configured for delivery to an exercise equipment associated with the activity. 
     
     
         11 . The method of  claim 10 , further comprising generating the set of control instructions upon inputting the performance dataset into a transformation model configured to return device settings, wherein the set of control instructions is configured to adjust settings of the exercise equipment used by the user in relation to performance of the activity. 
     
     
         12 . The method of  claim 1 , wherein the competitor comprises at least one of an entity separate from the user, and a historical performance of the activity by the user. 
     
     
         13 . A method comprising:
 receiving a set of signals related to user performance of an activity, from a garment worn by a user, the garment including a set of sensors configured to generate the set of signals;   extracting a performance dataset upon processing the set of signals, the performance dataset characterizing form and exertion features across a set of muscles of the user in association with performance of the activity;   generating, from the performance dataset, an analysis of the user performing the activity based upon a comparison to a reference profile associated with a goal of the user;   generating a recommended action based upon the analysis and executing the recommended action through an exercise feedback system in communication with the garment; and   promoting engagement between the user and the exercise feedback system based upon the analysis and in coordination with executing the recommended action.   
     
     
         14 . The method of  claim 13 , wherein the set of signals related to user performance of the activity comprises signals derived from one or more of: a subset of the set of muscles being activated, temporal aspects associated with activation of the subset of muscles, and intensity aspects associated with activation of the subset of muscles. 
     
     
         15 . The method of  claim 14 , wherein the set of signals further comprises signals derived from a set of environment sensors in an environment of the user, wherein the set of environment sensors comprises a temperature sensor and an altitude sensor. 
     
     
         16 . The method of  claim 14 , wherein the performance dataset is further derived from a contextual dataset derived from descriptions of the activity being performed. 
     
     
         17 . The method of  claim 13 , wherein the baseline activation is derived from a maximum exertion value of at least one of the set of muscles of the user. 
     
     
         18 . The method of  claim 13 , wherein the recommended action comprises a rendering of digital objects informative of user performance toward the goal at a second resolution more detailed than a first resolution associated with physical observation of the user, wherein the rendering is provided to a client device. 
     
     
         19 . The method of  claim 13 , wherein generating the recommended action comprises implementing a model trained with a training dataset derived from input data from the set of signals, a user profile, and performance of the user in response to a set of historical recommended actions provided to a population of users associated with the user profile and in relation to performing the activity. 
     
     
         20 . The method of  claim 13 , further comprising generating a set of control instructions upon inputting the performance dataset into a transformation model configured to return the set of control instructions for exercise equipment associated with the activity, wherein the set of control instructions is configured to adjust settings of the exercise equipment used by the user in relation to performance of the activity.